论文标题
基于光电储层计算的光子加固学习
Photonic reinforcement learning based on optoelectronic reservoir computing
论文作者
论文摘要
在没有培训数据（例如自动驾驶车辆，机器人控制，互联网广告和弹性光学网络）的情况下，在人工智能中进行了深入研究和开发强化学习。但是，深层神经网络增强学习的计算成本极高，降低学习成本是一个具有挑战性的问题。我们在实验和数值上提出了使用基于光电延迟的储层计算的光子在线实现。在拟议的方案中，我们以几种兆赫兹的速度加速了加固学习，因为在储层计算中没有内部连接权重的学习过程。我们执行两个基准任务，即CartPole-V0和Mountancar-V0任务，以评估所提出的方案。我们的结果代表了基于光子储层计算的强化学习的第一个硬件实施，并为快速有效的增强学习为新颖的光子加速器铺平了道路。
Reinforcement learning has been intensively investigated and developed in artificial intelligence in the absence of training data, such as autonomous driving vehicles, robot control, internet advertising, and elastic optical networks. However, the computational cost of reinforcement learning with deep neural networks is extremely high and reducing the learning cost is a challenging issue. We propose a photonic on-line implementation of reinforcement learning using optoelectronic delay-based reservoir computing, both experimentally and numerically. In the proposed scheme, we accelerate reinforcement learning at a rate of several megahertz because there is no required learning process for the internal connection weights in reservoir computing. We perform two benchmark tasks, CartPole-v0 and MountanCar-v0 tasks, to evaluate the proposed scheme. Our results represent the first hardware implementation of reinforcement learning based on photonic reservoir computing and pave the way for fast and efficient reinforcement learning as a novel photonic accelerator.
